Mapping the Spatial Heterogeneity of Watershed Ecosystems and Water Quality in Rainforest Fjordlands
Bibliographic record
Abstract
Small coastal watersheds (< 10,000 km 2 ) can play a large role in forming biogeochemical linkages between land and sea, yet the spatial heterogeneity of small watershed ecosystems is poorly understood due to sparse observations in many regions. In this study, we examined the spatial heterogeneity of water quality exported from diverse watersheds in two rainforest fjordland complexes. Samples were collected about monthly for a year from the outlets of 56 watersheds spanning from high mountains to low islands. Many (20) water quality properties varied significantly across six previously established watershed types defined by 12 easily computed geospatial variables. For example, organic matter concentrations ranged from very low in a Glacierized Mountains watershed type (1.2 ± 0.1 mg L −1 DOC; 28.5 ± 4.6 µg L −1 DON) to very high (15.1 ± 1.0 mg L −1 DOC; 215.6 ± 20.4 µg L −1 DON) in a Rain Lowlands type. Along this gradient, the dominant form of dissolved nitrogen switched from inorganic to organic and the dominant form of phosphorous switched from particulate to dissolved. Watershed type alone explained 67% of the variance in the first principal component of water quality (PC1) representing 20 water properties. Although underlying causes were likely complex, a great deal of spatial variation in water quality (for example, 91% of PC1) was predicted by simple measures of topography and climate (for example, elevation and mean annual precipitation). The physiographic structure of the coastal land mass appears to enable a complex mosaic of watershed ecosystems, which may affect meta-ecosystem function at the coastal margin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".